Borrowing historical data for use in clinical trials has increased in recent years. This is accomplished in the Bayesian framework by specification of informative prior distributions. One such approach is the robust mixture prior arising as a weighted mixture of an informative prior and a robust prior inducing dynamic borrowing that allows to borrow most when the current and external data are observed to be similar. The robust mixture prior requires the choice of three additional quantities: the mixture weight, and the mean and dispersion of the robust component. Some general guidance is available, but a case-by-case study of the impact of these quantities on specific operating characteristics seems lacking. We focus on evaluating the impact of parameter choices for the robust component of the mixture prior in one-arm and hybrid-control trials. The results show that all three quantities can strongly impact the operating characteristics. In particular, as already known, variance of the robust component is linked to robustness. Less known, however, is that its location can have a strong impact on Type I error rate and MSE which can even become unbounded. Further, the impact of the weight choice is strongly linked with the robust component's location and variance. Recommendations are provided for the choice of the robust component parameters, prior weight, alternative functional form for this component as well as considerations to keep in mind when evaluating operating characteristics.

翻译：近年来，在临床试验中借用历史数据的情况日益增多。这在贝叶斯框架中通过设定信息性先验分布来实现。其中一种方法是采用稳健混合先验，该先验由信息性先验与稳健先验的加权混合构成，能够实现动态借用：当观察到当前数据与外部数据相似时，借用量达到最大。稳健混合先验需要额外选择三个量：混合权重、稳健分量的均值与离散度。目前已有一些通用指导原则，但针对这些参数对具体操作特征影响的个案研究似乎尚显不足。本文重点评估在单臂试验和混合对照试验中，混合先验的稳健分量参数选择所产生的影响。结果表明，所有三个参数均能显著影响操作特征。特别地，如已知晓，稳健分量的方差与稳健性相关。然而较少被认识到的是，其位置参数可能对第一类错误率和均方误差产生强烈影响，甚至可能导致这些指标无界。此外，权重选择的影响与稳健分量的位置和方差密切相关。本文为稳健分量参数、先验权重的选择提供了建议，探讨了该分量的替代函数形式，并指出了评估操作特征时需注意的考量因素。